Draft Model Update
I have made significant changes to the draft model; it’s very refined at this point and much improved from the previous iteration. To avoid confusion, I’m going to make this the go-to post and take down all posts pertaining to the earlier versions. But I’m going to use a few quotes from them so they’re not lost.
From my post called “NBA Draft Projection Model and More”:
I’ve been working on developing a model that attempts to project NBA performance of college players. Basically I ran multiple linear regressions on pretty much all the data I could collect (like pace-adjusted box score numbers, team sos, measurements, etc.) for college players from 2002-2009 against their career NBA RAPM. I removed insignificant factors, and eventually came up with a fairly reasonable predictor.
From my post called “Draft Rankings!!”:
I’m obsessive by nature, so this certainly won’t be the final iteration, but I’m pretty happy with it at this point…
…The results are far from perfect as they probably always will be – remember what we’re doing here, we’re taking stats from college kids playing against varying levels of competition in a very limited sample size and trying to project their careers. But I feel pretty confident that we can make educated guesses with this data – and do a much better job than what we’ve actually seen in the past.
I made a number of changes, but the following is a brief summary of the most significant ones:
- I added in all players’ career numbers instead of using only their numbers from their final NCAA season. This turned out to be a very significant improvement, and I probably should have done it in the first place.
- I went back to using ONE regression rather than three different ones (for points, wings, and bigs – an idea I had taken from Hollinger). I was able to do this because of having the career numbers. This is important because all players can be more reasonably compared to each other regardless of position, and now everyone is on a sliding scale – for example, the difference between someone who plays a bit more SF than PF and someone who plays a bit more PF than SF is very small now, which is the way it should be.
- I refined and normalized the y values (the dependent variable for each player). Instead of using long-term RAPM, I used a RAPM-SPM blend based on the same relative period of time for each player.
Here are some observations about what player projections mean:
- Like before, players +2 or better are very likely to be all-star caliber NBA players. The +2 club is more exclusive than before, and so if there’s a +2 on the board when your team is picking, take him.
- +1 or better means the player is very likely to be a solid NBA player. In some cases – usually if the player has behavioral or work ethic issues – +1s won’t pan out, but more often than not, they will.
- Players in the positive or slightly negative range are more likely than not to be solid contributors.
- Players more than slightly negative will be hit and miss. You probably won’t find may great players here, and the more negative, the less likely it is the player will be any good.
Finally, here are all the out-of-sample (2010 to 2012) results for drafted players:
And as always, the current draft rankings can be found at the top bar.